Daily Archive
Updated from current HFEPX corpus (Feb 27, 2026). 12 papers are grouped in this daily page. Common evaluation modes: Automatic Metrics, Simulation Env. Common annotation unit: Multi Dim Rubric. Frequently cited benchmark: Retrieval. Common metric signal: accuracy. Use this page to compare protocol setup, judge behavior, and labeling design decisions before running new eval experiments. Newest paper in this set is from Oct 18, 2025.
Updated from current HFEPX corpus (Feb 27, 2026). This page tracks 12 papers for HFEPX Weekly Archive: 2025-W42. Dominant protocol signals include automatic metrics, simulation environments, with frequent benchmark focus on Retrieval, DROP and metric focus on accuracy, f1. Use the grounded sections below to prioritize reproducible protocol choices, benchmark-matched comparisons, and judge-vs-human evaluation checks.
16.7% of papers report explicit human-feedback signals, led by pairwise preferences.
Evidence: Precise Attribute Intensity Control in Large Language Models via Targeted Representation Editing , FrugalPrompt: Reducing Contextual Overhead in Large Language Models via Token Attribution , MNO: Multiscale Neural Operator for 3D Computational Fluid Dynamics , Detecting Early and Implicit Suicidal Ideation via Longitudinal and Information Environment Signals on Social Media
automatic metrics appears in 83.3% of papers in this hub.
Evidence: FrugalPrompt: Reducing Contextual Overhead in Large Language Models via Token Attribution , MNO: Multiscale Neural Operator for 3D Computational Fluid Dynamics , LUMI: Unsupervised Intent Clustering with Multiple Pseudo-Labels , Understanding the Ability of LLMs to Handle Character-Level Perturbation
Retrieval is a recurring benchmark anchor for cross-paper comparisons in this page.
Evidence: MemoTime: Memory-Augmented Temporal Knowledge Graph Enhanced Large Language Model Reasoning , Embedding-Based Context-Aware Reranker , PRoH: Dynamic Planning and Reasoning over Knowledge Hypergraphs for Retrieval-Augmented Generation , FrugalPrompt: Reducing Contextual Overhead in Large Language Models via Token Attribution
Quality-control reporting is sparse in this slice; prioritize papers with explicit calibration or adjudication steps.
Evidence: FrugalPrompt: Reducing Contextual Overhead in Large Language Models via Token Attribution , MNO: Multiscale Neural Operator for 3D Computational Fluid Dynamics , Detecting Early and Implicit Suicidal Ideation via Longitudinal and Information Environment Signals on Social Media , LUMI: Unsupervised Intent Clustering with Multiple Pseudo-Labels
Rater context is mostly unspecified rater pools, and annotation is commonly multi-dimensional rubrics; use this to scope replication staffing.
Evidence: FrugalPrompt: Reducing Contextual Overhead in Large Language Models via Token Attribution , MNO: Multiscale Neural Operator for 3D Computational Fluid Dynamics , Detecting Early and Implicit Suicidal Ideation via Longitudinal and Information Environment Signals on Social Media , LUMI: Unsupervised Intent Clustering with Multiple Pseudo-Labels
Stratify by benchmark (Retrieval vs DROP) before comparing methods.
Evidence: FrugalPrompt: Reducing Contextual Overhead in Large Language Models via Token Attribution , MNO: Multiscale Neural Operator for 3D Computational Fluid Dynamics , Detecting Early and Implicit Suicidal Ideation via Longitudinal and Information Environment Signals on Social Media , LUMI: Unsupervised Intent Clustering with Multiple Pseudo-Labels
Papers with explicit human feedback
Coverage is a replication risk (16.7% vs 45% target).
Papers reporting quality controls
Coverage is a replication risk (0% vs 30% target).
Papers naming benchmarks/datasets
Coverage is usable but incomplete (33.3% vs 35% target).
Papers naming evaluation metrics
Coverage is strong (58.3% vs 35% target).
Papers with known rater population
Coverage is a replication risk (0% vs 35% target).
Papers with known annotation unit
Coverage is usable but incomplete (25% vs 35% target).
1. FrugalPrompt: Reducing Contextual Overhead in Large Language Models via Token Attribution
Start here for detailed protocol reporting, including rater and quality-control evidence.
2. MNO: Multiscale Neural Operator for 3D Computational Fluid Dynamics
Start here for detailed protocol reporting, including rater and quality-control evidence.
Start here for detailed protocol reporting, including rater and quality-control evidence.
4. LUMI: Unsupervised Intent Clustering with Multiple Pseudo-Labels
Adds automatic metrics for broader coverage within this hub.
5. Understanding the Ability of LLMs to Handle Character-Level Perturbation
Adds automatic metrics for broader coverage within this hub.
6. Assessing Web Search Credibility and Response Groundedness in Chat Assistants
Adds simulation environments for broader coverage within this hub.
7. Closing the Gap Between Text and Speech Understanding in LLMs
Adds automatic metrics for broader coverage within this hub.
8. MemoTime: Memory-Augmented Temporal Knowledge Graph Enhanced Large Language Model Reasoning
Adds automatic metrics for broader coverage within this hub.
automatic_metrics vs simulation_env
both=0, left_only=10, right_only=2
0 papers use both Automatic Metrics and Simulation Env.
Benchmark Brief
Retrieval
Coverage: 3 papers (25%)
3 papers (25%) mention Retrieval.
Examples: MemoTime: Memory-Augmented Temporal Knowledge Graph Enhanced Large Language Model Reasoning , Embedding-Based Context-Aware Reranker , PRoH: Dynamic Planning and Reasoning over Knowledge Hypergraphs for Retrieval-Augmented Generation
Benchmark Brief
DROP
Coverage: 1 papers (8.3%)
1 papers (8.3%) mention DROP.
Examples: Closing the Gap Between Text and Speech Understanding in LLMs
Metric Brief
accuracy
Coverage: 3 papers (25%)
3 papers (25%) mention accuracy.
Examples: MNO: Multiscale Neural Operator for 3D Computational Fluid Dynamics , Embedding-Based Context-Aware Reranker , Precise Attribute Intensity Control in Large Language Models via Targeted Representation Editing
Metric Brief
f1
Coverage: 1 papers (8.3%)
1 papers (8.3%) mention f1.
Examples: PRoH: Dynamic Planning and Reasoning over Knowledge Hypergraphs for Retrieval-Augmented Generation
Metric Brief
faithfulness
Coverage: 1 papers (8.3%)
1 papers (8.3%) mention faithfulness.
Examples: MemoTime: Memory-Augmented Temporal Knowledge Graph Enhanced Large Language Model Reasoning
Most Cited In This Hub
Fast path to methods with the strongest citation traction in this scope.
Papers: FrugalPrompt: Reducing Contextual Overhead in Large Language Models via Token Attribution , MNO: Multiscale Neural Operator for 3D Computational Fluid Dynamics , Detecting Early and Implicit Suicidal Ideation via Longitudinal and Information Environment Signals on Social Media
Most Recent
Fast path to latest protocol changes and newly published evaluation setups.
Papers: FrugalPrompt: Reducing Contextual Overhead in Large Language Models via Token Attribution , MNO: Multiscale Neural Operator for 3D Computational Fluid Dynamics , Detecting Early and Implicit Suicidal Ideation via Longitudinal and Information Environment Signals on Social Media
Best Protocol Detail
Papers with explicit rater/unit metadata and quality-control signals for reproducibility.
Papers: FrugalPrompt: Reducing Contextual Overhead in Large Language Models via Token Attribution , MNO: Multiscale Neural Operator for 3D Computational Fluid Dynamics , Detecting Early and Implicit Suicidal Ideation via Longitudinal and Information Environment Signals on Social Media
Syed Rifat Raiyan, Md Farhan Ishmam, Abdullah Al Imran, Mohammad Ali Moni · Oct 18, 2025
Human communication heavily relies on laconism and inferential pragmatics, allowing listeners to successfully reconstruct rich meaning from sparse, telegraphic speech.
Qinxuan Wang, Chuang Wang, Mingyu Zhang, Jingwei Sun, Peipei Yang · Oct 17, 2025
We evaluate MNO on diverse benchmarks, covering steady-state and unsteady flow scenarios with up to 300k points.
Soorya Ram Shimgekar, Ruining Zhao, Agam Goyal, Violeta J. Rodriguez, Paul A. Bloom · Oct 16, 2025
On social media, several individuals experiencing suicidal ideation (SI) do not disclose their distress explicitly.
I-Fan Lin, Faegheh Hasibi, Suzan Verberne · Oct 16, 2025
Our evaluation on four benchmark sets shows that our approach achieves competitive results, better than recent state-of-the-art baselines, while avoiding the need to estimate the number of clusters during embedding refinement, as is require
Anyuan Zhuo, Xuefei Ning, Ningyuan Li, Jingyi Zhu, Yu Wang · Oct 16, 2025
Surprisingly, even under severe perturbation, such as shuffling nearly all words character-wise to produce text that is almost unreadable to humans, or inserting invisible characters which are several times more than the visible ones as noi
Ivan Vykopal, Matúš Pikuliak, Simon Ostermann, Marián Šimko · Oct 15, 2025
Chat assistants increasingly integrate web search functionality, enabling them to retrieve and cite external sources.
Santiago Cuervo, Skyler Seto, Maureen de Seyssel, Richard He Bai, Zijin Gu · Oct 15, 2025
Applied to 3B and 7B LLMs, SALAD achieves competitive performance with a strong open-weight model across broad-domain benchmarks in knowledge, language understanding, and reasoning, while training on over an order of magnitude less speech d
Xingyu Tan, Xiaoyang Wang, Qing Liu, Xiwei Xu, Xin Yuan · Oct 15, 2025
Comprehensive experiments on multiple temporal QA benchmarks show that MemoTime achieves overall state-of-the-art results, outperforming the strong baseline by up to 24.0%.
Ye Yuan, Mohammad Amin Shabani, Siqi Liu · Oct 15, 2025
We evaluate EBCAR against SOTA rerankers on the ConTEB benchmark, demonstrating its effectiveness for information retrieval requiring cross-passage inference and its advantages in both accuracy and efficiency.
Marisa C. Peczuh, Nischal Ashok Kumar, Ryan Baker, Blair Lehman, Danielle Eisenberg · Oct 14, 2025
As the world becomes increasingly saturated with AI-generated content, disinformation, and algorithmic persuasion, critical thinking - the capacity to evaluate evidence, detect unreliable claims, and exercise independent judgment - is becom
Xiangjun Zai, Xingyu Tan, Xiaoyang Wang, Qing Liu, Xiwei Xu · Oct 14, 2025
Experiments across multiple domains demonstrate that PRoH achieves state-of-the-art performance, surpassing the prior SOTA model HyperGraphRAG by an average of 19.73% in F1 and 8.41% in Generation Evaluation (G-E) score, while maintaining s
Rongzhi Zhang, Liqin Ye, Yuzhao Heng, Xiang Chen, Tong Yu · Oct 14, 2025
Finally, we demonstrate efficiency enhancements across three downstream tasks: preference data synthesis, Pareto frontier approximation and optimization, and distillation of aligned behaviors for intervention-free inference.
